18243348. NEURAL NETWORK PROMPT TUNING simplified abstract (NVIDIA Corporation)
Contents
- 1 NEURAL NETWORK PROMPT TUNING
NEURAL NETWORK PROMPT TUNING
Organization Name
Inventor(s)
Anima Anandkumar of Pasadena CA (US)
Weili Nie of Sunnyvale CA (US)
De-An Huang of Cupertino CA (US)
Zhiding Yu of Cupertino CA (US)
Manli Shu of Greenbelt MD (US)
NEURAL NETWORK PROMPT TUNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18243348 titled 'NEURAL NETWORK PROMPT TUNING
Simplified Explanation
The patent application describes apparatuses, systems, and techniques for performing neural networks, specifically selecting the most consistent output of pre-trained neural networks based on variances of inputs.
- Neural networks are pre-trained to perform specific tasks.
- The most consistent output of the neural networks is selected based on variances in the inputs.
- The selection process is at least partially based on the plurality of variances in the inputs.
Potential Applications
This technology could be applied in various fields such as:
- Image recognition
- Natural language processing
- Autonomous vehicles
Problems Solved
This technology helps in:
- Improving the accuracy of neural network outputs
- Enhancing the reliability of neural network predictions
Benefits
The benefits of this technology include:
- Increased efficiency in neural network operations
- Enhanced performance in complex tasks
- Improved decision-making based on neural network outputs
Potential Commercial Applications
A potential commercial application for this technology could be in:
- Healthcare for medical image analysis
- Finance for fraud detection
- Manufacturing for quality control
Possible Prior Art
One possible prior art could be the use of ensemble methods in machine learning to improve prediction accuracy by combining multiple models.
Unanswered Questions
How does this technology compare to existing methods for selecting neural network outputs based on input variances?
This article does not provide a direct comparison to existing methods for selecting neural network outputs based on input variances. Further research or a comparative study would be needed to address this question.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not discuss potential limitations or challenges of implementing this technology in real-world applications. Factors such as computational resources, data quality, and model complexity could be important considerations that need to be explored further.
Original Abstract Submitted
Apparatuses, systems, and techniques to perform neural networks. In at least one embodiment, a most consistent output of one or more pre-trained neural networks is to be selected. In at least one embodiment, a most consistent output of one or more pre-trained neural networks is to be selected based, at least in part, on a plurality of variances of one or more inputs to the one or more neural networks.
